Replication Materials for
"Non-ignorable Attrition in Pairwise Randomized Experiments"
by Kentaro Fukumoto
in Political Analysis, forthcoming
October 26, 2020

* Description: In pairwise randomized experiments, what if the outcomes of some units are missing? One solution is to delete missing units (the unitwise deletion estimator, UDE). If attrition is non-ignorable, however, the UDE is biased. Instead, scholars might employ the pairwise deletion estimator (PDE), which deletes the pairmates of missing units as well. This study proves that the PDE can be biased but more efficient than the UDE and, surprisingly, the conventional variance estimator of the PDE is unbiased in a super-population. I also propose a new variance estimator for the UDE and argue that it is easier to interpret the PDE as a causal effect than the UDE. To conclude, I recommend the PDE rather than the UDE.

* Operating system: Windows 10

* Processors: Intel(R) Core(TM) i7-9750H CPU @ 2.60 GHz 2.59 GHz (Six-Cores)

* RAM: 16.0 GB

* Programs: R (version 3.6.2)

* R packages required: none

* Data: Angrist, J., and V. Lavy. 2009. "The Effects of High Stakes High School Achievement Awards: Evidence From aRandomized Trial." American Economic Review 99 (4): 1384–1414, Table A1. [Copyright American Economic Association; reproduced with permission of the American Economic Review]

* Process of replication: If you implement replication.r, you will obtain Table 1 and Figures 1 through 3 in the Supplementary Material (pp. 132-135).

* Running time: less than one second

* Most Recent Date of Successful Replication: October 26, 2020